Type-Specific Rule-Based Generation of Semantic Variants of Natural Language Expression

ABSTRACT

A mechanism is provided in a data processing system having a processor and a memory storing a store of semantic types and instructions for implementing a natural language processing engine for generating semantically equivalent variants of a natural language term. The mechanism receives an input term for variant analysis. The natural language processing engine executing on the data processing system identifies a semantic type of the input term based on a store of semantic types. The natural language processing engine performs a type-specific series of rule-based expansions of the input term based on the identified semantic type of the input term to form a set of semantically equivalent variants of the input term. The natural language processing engine performs a natural language processing operation using the input term and the set of semantically equivalent variants of the input term.

GOVERNMENT RIGHTS

This invention was made with United States Government support undercontract number 2013-12101100008. THE GOVERNMENT HAS CERTAIN RIGHTS INTHIS INVENTION.

BACKGROUND

The present application relates generally to an improved data processingapparatus and method and more specifically to mechanisms for type-basedgeneration of semantic variants of natural language expressions forstring or regular expression matching.

With the increased usage of computing networks, such as the Internet,humans are currently inundated and overwhelmed with the amount ofinformation available to them from various structured and unstructuredsources. However, information gaps abound as users try to piece togetherwhat they can find that they believe to be relevant during searches forinformation on various subjects. To assist with such searches, recentresearch has been directed to generating Question and Answer (QA)systems which may take an input question, analyze it, and return resultsindicative of the most probable answer to the input question. QA systemsprovide automated mechanisms for searching through large sets of sourcesof content, e.g., electronic documents, and analyze them with regard toan input question to determine an answer to the question and aconfidence measure as to how accurate an answer is for answering theinput question.

Examples of QA systems are the IBM Watson™ system available fromInternational Business Machines (IBM®) Corporation of Armonk, N.Y.,Siri® from Apple®, and Cortana® from Microsoft®. The IBM Watson™ systemis an application of advanced natural language processing, informationretrieval, knowledge representation and reasoning, and machine learningtechnologies to the field of open domain question answering. The IBMWatson™ system is built on IBM's DeepQA™ technology used for hypothesisgeneration, massive evidence gathering, analysis, and scoring. DeepQA™takes an input question, analyzes it, decomposes the question intoconstituent parts, generates one or more hypotheses based on thedecomposed question and results of a primary search of answer sources,performs hypothesis and evidence scoring based on a retrieval ofevidence from evidence sources, performs synthesis of the one or morehypotheses, and based on trained models, performs a final merging andranking to output an answer to the input question along with aconfidence measure.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described herein in the DetailedDescription. This Summary is not intended to identify key factors oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

In one illustrative embodiment, a method is provided in a dataprocessing system having a processor and a memory storing a store ofsemantic types and instructions for implementing a natural languageprocessing engine for generating semantically equivalent variants of anatural language term. The method comprises receiving an input term forvariant analysis. The method further comprises identifying, by thenatural language processing engine executing on the data processingsystem, a semantic type of the input term based on a store of semantictypes. The method further comprises performing, by the natural languageprocessing engine, a type-specific series of rule-based expansions ofthe input term based on the identified semantic type of the input termto form a set of semantically equivalent variants of the input term. Themethod further comprises performing, by the natural language processingengine, a natural language processing operation using the input term andthe set of semantically equivalent variants of the input term.

In other illustrative embodiments, a computer program product comprisinga computer usable or readable medium having a computer readable programis provided. The computer readable program, when executed on a computingdevice, causes the computing device to perform various ones of, andcombinations of, the operations outlined above with regard to the methodillustrative embodiment.

In yet another illustrative embodiment, a system/apparatus is provided.The system/apparatus may comprise one or more processors and a memorycoupled to the one or more processors. The memory may compriseinstructions which, when executed by the one or more processors, causethe one or more processors to perform various ones of, and combinationsof, the operations outlined above with regard to the method illustrativeembodiment.

These and other features and advantages of the present invention will bedescribed in, or will become apparent to those of ordinary skill in theart in view of, the following detailed description of the exampleembodiments of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention, as well as a preferred mode of use and further objectivesand advantages thereof, will best be understood by reference to thefollowing detailed description of illustrative embodiments when read inconjunction with the accompanying drawings, wherein:

FIG. 1 depicts a schematic diagram of one illustrative embodiment of anatural language processing system in a computer network;

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments are implemented;

FIG. 3 illustrates a natural language processing system pipeline forprocessing an input question in accordance with one illustrativeembodiment:

FIG. 4 is a block diagram of a type-based semantic variant generationcomponent in accordance with an illustrative embodiment;

FIG. 5 depicts a view of a user interface for automatic type-basedgeneration of semantic variants of a natural language expression inaccordance with an illustrative embodiment;

FIGS. 6A-6C illustrate answer alternatives based on a plurality ofpolicies in accordance with an illustrative embodiment; and

FIG. 7 is a flowchart illustrating operation of a type-based semanticvariant generation component in accordance with an illustrativeembodiment.

DETAILED DESCRIPTION

The illustrative embodiments provide mechanisms for automaticallygenerating semantic variants of natural language names and expressionsdenoting quantities, for use in creating answer keys for automaticquestion answering systems. Variants are based on a canonical answerwith its expression type input by a user. For example, for the canonicalinput “2” and the specified expression type MEASURE-LINEAR[Feet], themechanisms provide variants, such as “24 inches” and “two feet.”

A deep question answering system generates answer candidates from textpassages and then passes the answer candidates to a scoring module thatranks the answer candidates with the top ranked candidate returned asthe answer to the question. These answers are textual strings extractedfrom the text passages. In order to evaluate and train such systems, thereturned answer must be classified as true or false. Answer key sets ofquestion/answer pairs (QA pairs), typically developed manually, are usedfor this task. In addition to their use as training data, these QA pairsprovide the basis for calculating accuracy metrics to evaluateperformance of the question answering system.

Since a correct answer to even a factual question may have a number ofsemantically equivalent variants, to properly score the answer ascorrect or incorrect, these semantic variants must be included in theanswer key. Providing full sets of variants is a labor-intensive anderror prone activity, thus the need for an automatic mechanism forgenerating variants.

When creating a question and answer pair set for a question answeringsystem, the answer should specify all true answers to a given question.For answers referring to particular entities, there is a vast range ofcorrect answers. This is true even for questions with single distinctanswers, such as answers specifying a particular numerically measuredvalue (“12 ounces”) or a specific date (“July 4^(th), 1776”). Toillustrate, a date—even in fully specified digit-only form—can bewritten in various formats (e.g., “1776-7-4,” “7-4-1776”) with variousdifferent separators (“03/29/1991,” “03-29-1991,” “03.29.1991.” etc.)and the presence or absence of leading zeroes. Other format variationsinclude the use of ordinals (“1st,” “2nd,” “3rd,” etc.), abbreviation ofmonth names (“Jan,” “Feb,” “Mar,” etc.), abbreviation of years (“'12,”“'99,” etc.), and spelling out of years (“two thousand ten,” “twentyten,” etc.). Turning to expressions, such as “16 ounces,” there areexpression variants (“16 oz.”) unit variants (“1 pound,” “1 lb.”) andunit conversion (“453 grams”) and their variants (“0.453 kg”).Enumerating all of these expressions manually (even using abbreviationconventions such as the language of regular expressions) is a timeconsuming and error prone process.

The illustrative embodiments address the sweet spot in the generation ofanswer variants for scoring a question answering system between theenumeration of all possible orthographic and semantic variants and themanual creation of a list of acceptable alternatives. The illustrativeembodiments address the important need to identify a wide range—ideallyall—of the acceptable variant answers to a question with a minimum ofhuman effort. While some of the problems the illustrative embodimentsaddress could be partially addressed through more sophisticatednormalization at the time of answer generation or merging, even aperfect method for normalization would not obviate the need for themechanisms of the illustrative embodiments.

First, it is infeasible to presume that subject matter experts whogenerate question and answer pair sets would always be aware of theparticularities of the normalization strategy implemented in an answermerging process, and it certainly would be an additional burden on thequestion and answer writer to presume that they are. Second, general andautomatic normalization strategies may be inappropriate to particularquestions and their answers and so question-specific alternativegeneration policies are needed. The invention is a method for automaticexpansion that is user-configurable in two ways. The administrator canconfigure families of policies for expansion from which the subjectmatter expert, in the process of generating a particular question, canselect. In addition, particular variants automatically generated aresubject to human review and editing.

The illustrative embodiments provide mechanisms for automaticallygenerating a type-dependent semantic variants of a natural languageexpression for string or regular expression matching, such as forquestion/answer pairs that are used for ground truth in training aquestion answering cognitive system. The mechanisms determine to whichof a user-configurable list of high-level semantic types and subtypes aninput expression belongs (e.g., date, name, number, currency, measure,string). In one embodiment, the mechanisms perform user verificationand/or correction of the identified type and subtype. The mechanismsperform a type-specific series of rule-based expansions to generatesemantic variants. In some embodiments, the mechanisms performnormalization of regular expressions to account for special characters.The mechanisms provide a graphical user interface (GUI) that allows theuser to generate the semantic variants and edit the list of variants byadding or removing entries. Then, for a given question, the mechanismsgenerate an answer specification that references the answer expressionand the list of semantic variants.

Before beginning the discussion of the various aspects of theillustrative embodiments in more detail, it should first be appreciatedthat throughout this description the term “mechanism” will be used torefer to elements of the present invention that perform variousoperations, functions, and the like. A “mechanism,” as the term is usedherein, may be an implementation of the functions or aspects of theillustrative embodiments in the form of an apparatus, a procedure, or acomputer program product. In the case of a procedure, the procedure isimplemented by one or more devices, apparatus, computers, dataprocessing systems, or the like. In the case of a computer programproduct, the logic represented by computer code or instructions embodiedin or on the computer program product is executed by one or morehardware devices in order to implement the functionality or perform theoperations associated with the specific “mechanism.” Thus, themechanisms described herein may be implemented as specialized hardware,software executing on general purpose hardware, software instructionsstored on a medium such that the instructions are readily executable byspecialized or general purpose hardware, a procedure or method forexecuting the functions, or a combination of any of the above.

The present description and claims may make use of the terms “a,” “atleast one of,” and “one or more of” with regard to particular featuresand elements of the illustrative embodiments. It should be appreciatedthat these terms and phrases are intended to state that there is atleast one of the particular feature or element present in the particularillustrative embodiment, but that more than one can also be present.That is, these terms/phrases are not intended to limit the descriptionor claims to a single feature/element being present or require that aplurality of such features/elements be present. To the contrary, theseterms/phrases only require at least a single feature/element with thepossibility of a plurality of such features/elements being within thescope of the description and claims.

Moreover, it should be appreciated that the use of the term “engine,” ifused herein with regard to describing embodiments and features of theinvention, is not intended to be limiting of any particularimplementation for accomplishing and/or performing the actions, steps,processes, etc., attributable to and/or performed by the engine. Anengine may be, but is not limited to, software, hardware and/or firmwareor any combination thereof that performs the specified functionsincluding, but not limited to, any use of a general and/or specializedprocessor in combination with appropriate software loaded or stored in amachine readable memory and executed by the processor. Further, any nameassociated with a particular engine is, unless otherwise specified, forpurposes of convenience of reference and not intended to be limiting toa specific implementation. Additionally, any functionality attributed toan engine may be equally performed by multiple engines, incorporatedinto and/or combined with the functionality of another engine of thesame or different type, or distributed across one or more engines ofvarious configurations.

In addition, it should be appreciated that the following descriptionuses a plurality of various examples for various elements of theillustrative embodiments to further illustrate example implementationsof the illustrative embodiments and to aid in the understanding of themechanisms of the illustrative embodiments. These examples are intendedto be non-limiting and are not exhaustive of the various possibilitiesfor implementing the mechanisms of the illustrative embodiments. It willbe apparent to those of ordinary skill in the art in view of the presentdescription that there are many other alternative implementations forthese various elements that may be utilized in addition to, or inreplacement of, the examples provided herein without departing from thespirit and scope of the present invention.

The illustrative embodiments may be utilized in many different types ofdata processing environments. In order to provide a context for thedescription of the specific elements and functionality of theillustrative embodiments, FIGS. 1-3 are provided hereafter as exampleenvironments in which aspects of the illustrative embodiments may beimplemented. It should be appreciated that FIGS. 1-3 are only examplesand are not intended to assert or imply any limitation with regard tothe environments in which aspects or embodiments of the presentinvention may be implemented. Many modifications to the depictedenvironments may be made without departing from the spirit and scope ofthe present invention.

FIGS. 1-3 are directed to describing an example natural language (NL)processing system, such as a Question Answering (QA) system (alsoreferred to as a Question/Answer system or Question and Answer system),methodology, and computer program product with which the mechanisms ofthe illustrative embodiments are implemented. As will be discussed ingreater detail hereafter, the illustrative embodiments are integratedin, augment, and extend the functionality of these NL processingmechanisms.

With respect to the example embodiment of a QA system, it is importantto first have an understanding of how question answering in a QA systemis implemented before describing how the mechanisms of the illustrativeembodiments are integrated in and augment such QA systems. It should beappreciated that the QA mechanisms described in FIGS. 1-3 are onlyexamples and are not intended to state or imply any limitation withregard to the type of natural language processing mechanisms with whichthe illustrative embodiments are implemented. Many modifications to theexample NL processing system shown in FIGS. 1-3 may be implemented invarious embodiments of the present invention without departing from thespirit and scope of the present invention.

As an overview, a Question Answering system (QA system) is an artificialintelligence application executing on data processing hardware thatanswers questions pertaining to a given subject-matter domain presentedin natural language. The QA system receives inputs from various sourcesincluding input over a network, a corpus of electronic documents orother data, data from a content creator, information from one or morecontent users, and other such inputs from other possible sources ofinput. Data storage devices store the corpus of data. A content creatorcreates content in a document for use as part of a corpus of data withthe QA system. The document may include any file, text, article, orsource of data for use in the QA system. For example, a QA systemaccesses a body of knowledge about the domain, or subject matter area,e.g., financial domain, medical domain, legal domain, etc., where thebody of knowledge (knowledgebase) can be organized in a variety ofconfigurations, e.g., a structured repository of domain-specificinformation, such as ontologies, or unstructured data related to thedomain, or a collection of natural language documents about the domain.

Content users input questions to the QA system which then answers theinput questions using the content in the corpus of data by evaluatingdocuments, sections of documents, portions of data in the corpus, or thelike. When a process evaluates a given section of a document forsemantic content, the process can use a variety of conventions to querysuch document from the QA system, e.g., sending the query to the QAsystem as a well-formed question, which is then interpreted by the QAsystem and providing a response containing one or more answers to thequestion. Semantic content is content based on the relation betweensignifiers, such as words, phrases, signs, and symbols, and what theystand for, their denotation, or connotation. In other words, semanticcontent is content that interprets an expression, such as by usingNatural Language Processing.

As will be described in greater detail hereafter, the QA system receivesan input question, analyzes the question to extract the major elementsof the question, uses the extracted element to formulate queries, andthen applies those queries to the corpus of data. Based on theapplication of the queries to the corpus of data, the QA systemgenerates a set of hypotheses, or candidate answers to the inputquestion, by looking across the corpus of data for portions of thecorpus of data that have some potential for containing a valuableresponse to the input question. The QA system then performs deepanalysis, e.g., English Slot Grammar (ESG) and Predicate ArgumentStructure (PAS) builder, on the language of the input question and thelanguage used in each of the portions of the corpus of data found duringthe application of the queries using a variety of scoring algorithms.There may be hundreds or even thousands of scoring algorithms applied,each of which performs different analysis, e.g., comparisons, naturallanguage analysis, lexical analysis, or the like, and generates a score.For example, some scoring algorithms may look at the matching of termsand synonyms within the language of the input question and the foundportions of the corpus of data. Other scoring algorithms may look attemporal or spatial features in the language, while others may evaluatethe source of the portion of the corpus of data and evaluate itsveracity.

The scores obtained from the various scoring algorithms indicate theextent to which the potential response is likely to be a correct answerto the input question based on the specific area of focus of thatscoring algorithm. Each resulting score is then weighted against astatistical model, which is used to compute the confidence that the QAsystem has regarding the evidence for a candidate answer being thecorrect answer to the question. This process is repeated for each of thecandidate answers until the QA system identifies candidate answers thatsurface as being significantly stronger than others and thus, generatesa final answer, or ranked set of answers, for the input question.

As mentioned above, QA systems and mechanisms operate by accessinginformation from a corpus of data or information (also referred to as acorpus of content), analyzing it, and then generating answer resultsbased on the analysis of this data. Accessing information from a corpusof data typically includes: a database query that answers questionsabout what is in a collection of structured records, and a search thatdelivers a collection of document links in response to a query against acollection of unstructured data (text, etc.). Conventional questionanswering systems are capable of generating answers based on the corpusof data and the input question, verifying answers to a collection ofquestions from the corpus of data, and selecting answers to questionsfrom a pool of potential answers, i.e., candidate answers.

Content creators, such as article authors, electronic document creators,web page authors, document database creators, and the like, determineuse cases for products, solutions, and services described in suchcontent before writing their content. Consequently, the content creatorsknow what questions the content is intended to answer in a particulartopic addressed by the content. Categorizing the questions, such as interms of roles, type of information, tasks, or the like, associated withthe question, in each document of a corpus of data allows the QA systemto more quickly and efficiently identify documents containing contentrelated to a specific query. The content may also answer other questionsthat the content creator did not contemplate that may be useful tocontent users. The questions and answers may be verified by the contentcreator to be contained in the content for a given document. Thesecapabilities contribute to improved accuracy, system performance,machine learning, and confidence of the QA system. Content creators,automated tools, or the like, annotate or otherwise generate metadatafor providing information usable by the QA system to identify thesequestion-and-answer attributes of the content.

Operating on such content, the QA system generates answers for inputquestions using a plurality of intensive analysis mechanisms whichevaluate the content to identify the most probable answers, i.e.candidate answers, for the input question. The most probable answers areoutput as a ranked listing of candidate answers ranked according totheir relative scores or confidence measures calculated duringevaluation of the candidate answers, as a single final answer having ahighest ranking score or confidence measure, or which is a best match tothe input question, or a combination of ranked listing and final answer.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of anatural language processing system 100 in a computer network 102 inaccordance with an illustrative embodiment. One example of aquestion/answer generation which may be used in conjunction with theprinciples described herein is described in U.S. Patent ApplicationPublication No. 2011/0125734, which is herein incorporated by referencein its entirety. The NL processing system 100 is implemented on one ormore computing devices 104 (comprising one or more processors and one ormore memories, and potentially any other computing device elementsgenerally known in the art including buses, storage devices,communication interfaces, and the like) connected to the computernetwork 102. The network 102 includes multiple computing devices 104 incommunication with each other and with other devices or components viaone or more wired and/or wireless data communication links, where eachcommunication link comprises one or more of wires, routers, switches,transmitters, receivers, or the like. In the depicted example, NLprocessing system 100 and network 102 enables question/answer (QA)generation functionality for one or more QA system users via theirrespective computing devices 110-112. Other embodiments of the NLprocessing system 100 may be used with components, systems, sub-systems,and/or devices other than those that are depicted herein.

The NL processing system 100 is configured to implement an NL systempipeline 108 that receive inputs from various sources. For example, theNL processing system 100 receives input from the network 102, a corpusof electronic documents 106, NL system users, and/or other data andother possible sources of input. In one embodiment, some or all of theinputs to the NL processing system 100 are routed through the network102. The various computing devices 104 on the network 102 include accesspoints for content creators and NL system users. Some of the computingdevices 104 include devices for a database storing the corpus of data106 (which is shown as a separate entity in FIG. 1 for illustrativepurposes only). Portions of the corpus of data 106 may also be providedon one or more other network attached storage devices, in one or moredatabases, or other computing devices not explicitly shown in FIG. 1.The network 102 includes local network connections and remoteconnections in various embodiments, such that the NL processing system100 may operate in environments of any size, including local and global,e.g., the Internet.

In one embodiment, the content creator creates content in a document ofthe corpus of data 106 for use as part of a corpus of data with the NLprocessing system 100. The document includes any file, text, article, orsource of data for use in the NL processing system 100. NL system usersaccess the NL processing system 100 via a network connection or anInternet connection to the network 102, and input questions to the NLprocessing system 100 that are answered by the content in the corpus ofdata 106. In one embodiment, the questions are formed using naturallanguage. The NL processing system 100 analyzes and interprets thequestion, and provides a response to the NL system user, e.g., NLprocessing system user 110, containing one or more answers to thequestion. In some embodiments, the NL processing system 100 provides aresponse to users in a ranked list of candidate answers while in otherillustrative embodiments, the NL processing system 100 provides a singlefinal answer or a combination of a final answer and ranked listing ofother candidate answers.

The NL processing system 100 implements a NL system pipeline 108 whichcomprises a plurality of stages for processing an input question and thecorpus of data 106. The NL processing system pipeline 108 generatesanswers for the input question based on the processing of the inputquestion and the corpus of data 106. The NL processing system pipeline108 will be described in greater detail hereafter with regard to FIG. 3.

In some illustrative embodiments, the NL processing system 100 may bethe IBM Watson™ QA system available from International Business MachinesCorporation of Armonk, N.Y., which is augmented with the mechanisms ofthe illustrative embodiments described hereafter. As outlinedpreviously, the IBM Watson™ QA system receives an input question whichit then analyzes to extract the major features of the question, that inturn are then used to formulate queries that are applied to the corpusof data. Based on the application of the queries to the corpus of data,a set of hypotheses, or candidate answers to the input question, aregenerated by looking across the corpus of data for portions of thecorpus of data that have some potential for containing a valuableresponse to the input question. The IBM Watson™ QA system then performsdeep analysis on the language of the input question and the languageused in each of the portions of the corpus of data found during theapplication of the queries using a variety of scoring algorithms. Thescores obtained from the various scoring algorithms are then weightedagainst a statistical model that summarizes a level of confidence thatthe IBM Watson™ QA system has regarding the evidence that the potentialresponse, i.e. candidate answer, is inferred by the question. Thisprocess is repeated for each of the candidate answers to generate rankedlisting of candidate answers which may then be presented to the userthat submitted the input question, or from which a final answer isselected and presented to the user. More information about the IBMWatson™ QA system may be obtained, for example, from the IBM Corporationwebsite, IBM Redbooks, and the like. For example, information about theIBM Watson™ QA system can be found in Yuan et al., “Watson andHealthcare,” IBM developerWorks, 2011 and “The Era of Cognitive Systems:An Inside Look at IBM Watson and How it Works” by Rob High, IBMRedbooks, 2012.

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments are implemented. Data processingsystem 200 is an example of a computer, such as server 104 or client 110in FIG. 1, in which computer usable code or instructions implementingthe processes for illustrative embodiments of the present invention arelocated. In one illustrative embodiment, FIG. 2 represents a servercomputing device, such as a server 104, which implements an NLprocessing system 100 and NL system pipeline 108 augmented to includethe additional mechanisms of the illustrative embodiments describedhereafter.

In the depicted example, data processing system 200 employs a hubarchitecture including north bridge and memory controller hub (NB/MCH)202 and south bridge and input/output (I/O) controller hub (SB/ICH) 204.Processing unit 206, main memory 208, and graphics processor 210 areconnected to NB/MCH 202. Graphics processor 210 is connected to NB/MCH202 through an accelerated graphics port (AGP).

In the depicted example, local area network (LAN) adapter 212 connectsto SB/ICH 204. Audio adapter 216, keyboard and mouse adapter 220, modem222, read only memory (ROM) 224, hard disk drive (HDD) 226, CD-ROM drive230, universal serial bus (USB) ports and other communication ports 232,and PCI/PCIe devices 234 connect to SB/ICH 204 through bus 238 and bus240. PCI/PCIe devices may include, for example, Ethernet adapters,add-in cards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 224 may be, for example, a flashbasic input/output system (BIOS).

HDD 226 and CD-ROM drive 230 connect to SB/ICH 204 through bus 240. HDD226 and CD-ROM drive 230 may use, for example, an integrated driveelectronics (IDE) or serial advanced technology attachment (SATA)interface. Super I/O (SIO) device 236 is connected to SB/ICH 204.

An operating system runs on processing unit 206. The operating systemcoordinates and provides control of various components within the dataprocessing system 200 in FIG. 2. As a client, the operating system is acommercially available operating system such as Microsoft® Windows 8®.An object-oriented programming system, such as the Java™ programmingsystem, may run in conjunction with the operating system and providescalls to the operating system from Java™ programs or applicationsexecuting on data processing system 200.

As a server, data processing system 200 may be, for example, an IBM®eServer™ System p® computer system, running the Advanced InteractiveExecutive (AIX®) operating system or the LINUX® operating system. Dataprocessing system 200 may be a symmetric multiprocessor (SMP) systemincluding a plurality of processors in processing unit 206.Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as HDD 226, and are loaded into main memory 208 for execution byprocessing unit 206. The processes for illustrative embodiments of thepresent invention are performed by processing unit 206 using computerusable program code, which is located in a memory such as, for example,main memory 208, ROM 224, or in one or more peripheral devices 226 and230, for example.

A bus system, such as bus 238 or bus 240 as shown in FIG. 2, iscomprised of one or more buses. Of course, the bus system may beimplemented using any type of communication fabric or architecture thatprovides for a transfer of data between different components or devicesattached to the fabric or architecture. A communication unit, such asmodem 222 or network adapter 212 of FIG. 2, includes one or more devicesused to transmit and receive data. A memory may be, for example, mainmemory 208, ROM 224, or a cache such as found in NB/MCH 202 in FIG. 2.

Those of ordinary skill in the art will appreciate that the hardwaredepicted in FIGS. 1 and 2 may vary depending on the implementation.Other internal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives and the like, maybe used in addition to or in place of the hardware depicted in FIGS. 1and 2. Also, the processes of the illustrative embodiments may beapplied to a multiprocessor data processing system, other than the SMPsystem mentioned previously, without departing from the spirit and scopeof the present invention.

Moreover, the data processing system 200 may take the form of any of anumber of different data processing systems including client computingdevices, server computing devices, a tablet computer, laptop computer,telephone or other communication device, a personal digital assistant(PDA), or the like. In some illustrative examples, data processingsystem 200 may be a portable computing device that is configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data, for example. Essentially, dataprocessing system 200 may be any known or later developed dataprocessing system without architectural limitation.

FIG. 3 illustrates a natural language processing system pipeline forprocessing an input question in accordance with one illustrativeembodiment. The natural language (N L) processing system pipeline ofFIG. 3 may be implemented, for example, as NL system pipeline 108 of NLprocessing system 100 in FIG. 1. It should be appreciated that thestages of the NL processing system pipeline shown in FIG. 3 areimplemented as one or more software engines, components, or the like,which are configured with logic for implementing the functionalityattributed to the particular stage. Each stage is implemented using oneor more of such software engines, components or the like. The softwareengines, components, etc. are executed on one or more processors of oneor more data processing systems or devices and utilize or operate ondata stored in one or more data storage devices, memories, or the like,on one or more of the data processing systems. The NL system pipeline ofFIG. 3 is augmented, for example, in one or more of the stages toimplement the improved mechanism of the illustrative embodimentsdescribed hereafter, additional stages may be provided to implement theimproved mechanism, or separate logic from the pipeline 300 may beprovided for interfacing with the pipeline 300 and implementing theimproved functionality and operations of the illustrative embodiments.

In the depicted example, NL system pipeline 300 is implemented in aQuestion Answering (QA) system. The description that follows refers tothe NL system pipeline or the NL system pipeline as a QA system;however, aspects of the illustrative embodiments may be applied to otherNL processing systems, such as Web search engines that return semanticpassages from a corpus of documents.

As shown in FIG. 3, the NL system pipeline 300 comprises a plurality ofstages 310-390 through which the NL system operates to analyze an inputquestion and generate a final response. In an initial question inputstage, the NL system receives an input question 310 that is presented ina natural language format. That is, a user inputs, via a user interface,an input question 310 for which the user wishes to obtain an answer,e.g., “Who were Washington's closest advisors?” In response to receivingthe input question 310, the next stage of the NL system pipeline 300,i.e. the question and topic analysis stage 320, analyzes the inputquestion using natural language processing (NLP) techniques to extractmajor elements from the input question, and classify the major elementsaccording to types, e.g., names, dates, or any of a plethora of otherdefined topics. For example, in the example question above, the term“who” may be associated with a topic for “persons” indicating that theidentity of a person is being sought, “Washington” may be identified asa proper name of a person with which the question is associated,“closest” may be identified as a word indicative of proximity orrelationship, and “advisors” may be indicative of a noun or otherlanguage topic.

In addition, the extracted major features include key words and phrasesclassified into question characteristics, such as the focus of thequestion, the lexical answer type (LAT) of the question, and the like.As referred to herein, a lexical answer type (LAT) is a word in, or aword inferred from, the input question that indicates the type of theanswer, independent of assigning semantics to that word. For example, inthe question “What maneuver was invented in the 1500 s to speed up thegame and involves two pieces of the same color?,” the LAT is the string“maneuver.” The focus of a question is the part of the question that, ifreplaced by the answer, makes the question a standalone statement. Forexample, in the question “What drug has been shown to relieve thesymptoms of attention deficit disorder with relatively few sideeffects?,” the focus is “What drug” since if this phrase were replacedwith the answer it would generate a true sentence, e.g., the answer“Adderall” can be used to replace the phrase “What drug” to generate thesentence “Adderall has been shown to relieve the symptoms of attentiondeficit disorder with relatively few side effects.” The focus often, butnot always, contains the LAT. On the other hand, in many cases it is notpossible to infer a meaningful LAT from the focus.

Referring again to FIG. 3, the identified major elements of the questionare then used during a hypothesis generation stage 340 to decompose thequestion into one or more search queries that are applied by evidenceretrieval 351 to the corpora of data/information 345 in order togenerate one or more hypotheses. The queries are applied to one or moretext indexes storing information about the electronic texts, documents,articles, websites, and the like, that make up the corpus ofdata/information, e.g., the corpus of data 106 in FIG. 1. The queriesare applied to the corpus of data/information at the hypothesisgeneration stage 340 to generate results identifying potentialhypotheses for answering the input question, which can then beevaluated. That is, the application of the queries results in theextraction of portions of the corpus of data/information matching thecriteria of the particular query. These portions of the corpus are thenanalyzed and used in the hypothesis generation stage 340, to generatehypotheses for answering the input question 310. These hypotheses arealso referred to herein as “candidate answers” for the input question.For any input question, at this stage 340, there may be hundreds ofhypotheses or candidate answers generated that may need to be evaluated.

The NL system pipeline 300, in stage 350, then performs a deep analysisand comparison of the language of the input question and the language ofeach hypothesis or “candidate answer,” as well as performs evidencescoring to evaluate the likelihood that the particular hypothesis is acorrect answer for the input question. This involves evidence retrieval351, which retrieves passages from corpora 345.

Hypothesis and evidence scoring phase 350 uses a plurality of scoringalgorithms, each performing a separate type of analysis of the languageof the input question and/or content of the corpus that providesevidence in support of, or not in support of, the hypothesis. Eachscoring algorithm generates a score based on the analysis it performswhich indicates a measure of relevance of the individual portions of thecorpus of data/information extracted by application of the queries aswell as a measure of the correctness of the corresponding hypothesis,i.e. a measure of confidence in the hypothesis. There are various waysof generating such scores depending upon the particular analysis beingperformed. In general, however, these algorithms look for particularterms, phrases, or patterns of text that are indicative of terms,phrases, or patterns of interest and determine a degree of matching withhigher degrees of matching being given relatively higher scores thanlower degrees of matching.

For example, an algorithm may be configured to look for the exact termfrom an input question or synonyms to that term in the input question,e.g., the exact term or synonyms for the term “movie,” and generate ascore based on a frequency of use of these exact terms or synonyms. Insuch a case, exact matches will be given the highest scores, whilesynonyms may be given lower scores based on a relative ranking of thesynonyms as may be specified by a subject matter expert (person withknowledge of the particular domain and terminology used) orautomatically determined from frequency of use of the synonym in thecorpus corresponding to the domain. Thus, for example, an exact match ofthe term “movie” in content of the corpus (also referred to as evidence,or evidence passages) is given a highest score. A synonym of movie, suchas “motion picture” may be given a lower score but still higher than asynonym of the type “film” or “moving picture show.” Instances of theexact matches and synonyms for each evidence passage may be compiled andused in a quantitative function to generate a score for the degree ofmatching of the evidence passage to the input question.

Thus, for example, a hypothesis or candidate answer to the inputquestion of “What was the first movie?” is “The Horse in Motion.” If theevidence passage contains the statements “The first motion picture evermade was ‘The Horse in Motion’ in 1878 by Eadweard Muybridge. It was amovie of a horse running,” and the algorithm is looking for exactmatches or synonyms to the focus of the input question, i.e. “movie,”then an exact match of “movie” is found in the second sentence of theevidence passage and a highly scored synonym to “movie,” i.e. “motionpicture,” is found in the first sentence of the evidence passage. Thismay be combined with further analysis of the evidence passage toidentify that the text of the candidate answer is present in theevidence passage as well, i.e. “The Horse in Motion.” These factors maybe combined to give this evidence passage a relatively high score assupporting evidence for the candidate answer “The Horse in Motion” beinga correct answer.

It should be appreciated that this is just one simple example of howscoring can be performed. Many other algorithms of various complexitiesmay be used to generate scores for candidate answers and evidencewithout departing from the spirit and scope of the present invention.

In answer ranking stage 360, the scores generated by the various scoringalgorithms are synthesized into confidence scores or confidence measuresfor the various hypotheses. This process involves applying weights tothe various scores, where the weights have been determined throughtraining of the statistical model employed by the QA system and/ordynamically updated. For example, the weights for scores generated byalgorithms that identify exactly matching terms and synonyms may be setrelatively higher than other algorithms that evaluate publication datesfor evidence passages.

The weighted scores are processed in accordance with a statistical modelgenerated through training of the QA system that identifies a manner bywhich these scores may be combined to generate a confidence score ormeasure for the individual hypotheses or candidate answers. Thisconfidence score or measure summarizes the level of confidence that theQA system has about the evidence that the candidate answer is inferredby the input question, i.e. that the candidate answer is the correctanswer for the input question.

The resulting confidence scores or measures are processed by answerranking stage 360, which compares the confidence scores and measures toeach other, compares them against predetermined thresholds, or performsany other analysis on the confidence scores to determine whichhypotheses/candidate answers are the most likely to be the correctanswer to the input question. The hypotheses/candidate answers areranked according to these comparisons to generate a ranked listing ofhypotheses/candidate answers (hereafter simply referred to as “candidateanswers”).

Supporting evidence collection phase 370 collects evidence that supportsthe candidate answers from answer ranking phase 360. From the rankedlisting of candidate answers in stage 360 and supporting evidence fromsupporting evidence collection stage 370, NL system pipeline 300generates a final answer, confidence score, and evidence 380, or finalset of candidate answers with confidence scores and supporting evidence,and outputs answer, confidence, and evidence 390 to the submitter of theoriginal input question 310 via a graphical user interface or othermechanism for outputting information.

FIG. 4 is a block diagram of a type-based semantic variant generationcomponent in accordance with an illustrative embodiment. The semanticvariant generation component receives an input expression 401. Thesemantic variant generation component performs type detection (block402) on the input expression 401 to determine to which of a userconfigurable list of high-level semantic types and subtypes the inputexpression belongs. The types and subtypes (e.g., date, name, number,currency, measure, string) are stored in storage 408. The semanticvariant generation component performs user verification and/orcorrection of the identified type and subtype (block 403).

The semantic variant generation component interprets the type andsubtype (block 404) of the input expression 401. The semantic variantgeneration component determines (block 405) a type-specific series ofrule-based expansions. The type-specific rule-based expansions populatetype-specific expansion templates at a level of specificity based on theinput.

If the input expression 401 is identified with a measure type, thesemantic variant generation component performs unit conversion (block406) based on conversions among units stored in storage 408. Then, thesemantic variant generation component determines unit label variants(block 407) based on variants of unit names stored in storage 408. Themeasure type includes the following subtypes: Linear, Temporal,Mass/Weight, Volume, etc. For measurements, the semantic variantgeneration component would treat all subtypes similarly by creatingvariations in the single set of units and then extending that byconverting the measurement into other applicable units and outputtingvariations on those. The semantic variants for the measure type includethe following:

-   -   Punctuation (9,000, 9 000, 9000, one hundred sixty five, one        hundred sixty-five, etc.);    -   Large number word/digit combinations (1.2 million, 3 billion);    -   Number of decimal places (100.000, 100.00, 1.25 million, 1.3        million, 1 million, etc.);    -   Number as word (nine thousand, one hundred sixty-five);    -   Unit labels (meters, meter, m, miles, mi., etc.);    -   Conversion between different unit types (1 foot, 12 inches, 0.33        meters, etc.).

Then, the semantic variant generation component determines numbervariants (block 409). The determination of number variants in block 409may include variants in rounding, degree of precision, formatting, andword number variants. Then, the semantic variant generation componentdetermines number variants (block 409). The determination of numbervariants in block 409 may include variants in rounding, degree ofprecision, formatting, and word number variants. The number typeincludes the following subtypes: Integer, Float, Ordinal. The semanticvariants for the number type include the following:

-   -   Negative numbers (minus, negative, −);    -   Punctuation (9,000, 9 000, 9000, one hundred sixty five, one        hundred sixty-five, 15th, 15^(th) etc.);    -   Large number word/digit combinations (1.2 million, 3 billion, 2        billionth);    -   Number of decimal places (100.000, 100.00, 1.25 million, 1.3        million, 1 million, etc.);    -   Number as word (nine thousand, one hundred sixty-five, second,        fifteenth).

The semantic variant generation component performs normalization ofregular expressions (block 410) to account for special characters,resulting in output expression 414.

If the input expression 401 is identified with a currency type, thesemantic variant generation component determines unit label variants(block 407) based on variants of unit names stored in storage 408. Thecurrency type includes the following example subtypes: U.S. Dollars,Australian Dollars, Euros, Singapore Dollars, etc. The semantic variantsfor the currency type include the following:

-   -   Negative numbers (minus, negative, −);    -   Punctuation ($9,000, $9 000, $9000, $9.000, etc.);    -   Currency Identifiers (US$, USD$, $, dollars, etc.);    -   Large number word/digit combinations ($1.2 million, $3 billion);    -   Number of decimal places ($9,000.00, $9000, $1.25 million, $1.3        million, $1 million, etc.);    -   Number as word (nine thousand dollars, nine thousand dollars and        zero cents, etc.).

The semantic variant generation component performs normalization ofregular expressions (block 410) to account for special characters,resulting in output expression 414.

If the input expression 401 is identified with a string type, thesemantic variant generation component determines word variants (block411). Then, the semantic variant generation component performsnormalization of regular expressions (block 410) to account for specialcharacters, resulting in output expression 414.

If the input expression 401 is identified with a name type, the semanticvariant generation component determines name variants (block 412). Thename type supports up to three names (first, middle, last), prefix,suffix, honorifics, etc. Any information that is not included, such asmiddle name, will not be included in the semantic variants. The semanticvariants for the name type include the following:

-   -   Full name with middle (Johnathan Quentin Public);    -   Full name with middle initial and variations on punctuation        (Johnathan Q. Public, Johnathan Q Public, etc.);    -   Full name without middle name (Johnathan Public);    -   Last name only (Public);    -   Common nicknames (John Public);    -   Prefixes, suffixes, and honorifics (John Q. Public Jr., John        Quentin Public III, John Q. Public, Esquire, etc.).

Then, the semantic variant generation component performs normalizationof regular expressions (block 410) to account for special characters,resulting in output expression 414.

If the input expression 401 is identified with a date type, the semanticvariant generation component determines date variants (block 413). Thedate type includes the following example subtypes: General, Day, Month,Year, Day of Week, etc. For dates, each subtype would be treateddifferently. The General subtype would be the most varied, because itincludes all combinations of day-month-year, day-month, month-year, andyear. The Day subtype would include all variations of day-month-year.The Month subtype would include all variations of month-year and year.The Year subtype would include all variations of year. The Day of Weeksubtype is fairly straight-forward; it would create variants on days ofthe week. For all date types, if any piece of information is missing,the system would create as many variants as possible with the includedinformation. The semantic variants for the date type include thefollowing:

-   -   Punctuation (03/29/1991, 03-29-1991, 03.29.1991, '12, Jul.,        Tues., twenty ten, twenty-ten, etc.);    -   Ordinal numbers (July 1st, September 14th, October 2nd, etc.);    -   Date format (12/1/2001, 31/1/2001, 2014/12/1, etc.);    -   Year in words (nineteen-eighteen, two thousand two, twenty-ten,        two thousand ten, etc.);    -   Abbreviations (Jul, Sept, Mar, Tues, Mon, etc.).

Then, the semantic variant generation component performs normalizationof regular expressions (block 410) to account for special characters,resulting in output expression 414.

Other types not shown in FIG. 4 may include Percent and Range. Thesemantic variants for the percent type include the following:

-   -   Negative numbers (minus, negative, −);    -   Punctuation (9,000%, 9 000%, 9.000%, etc.);    -   Percent identifier (percent, percent, %);    -   Large number word/digit combinations (1.2 million, 3 billion);    -   Number of decimal places (100.000, 100.00, 1.25 million, 1.3        million, 1 million, etc.);    -   Number as word (nine thousand percent, nine thousand percent).

The semantic variants for the range type include Currency-USD,Currency-AUD, Currency-EUR, Currency-SGD, Currency-Other, Percent,Date-General, Date-Day, Date-Month, Date-Year, Date-Day of Week,Number-Integer, Number-Float, Measurement-Linear, Measurement-Temporal,Measurement-Mass/Weight, Measurement-Volume, Measurement-Other, etc. Forranges, the semantic variant generation component would break apart therange, create variants for each side using the above types, and thencombine those variants using different range keywords. The semanticvariants for the range type include the following:

-   -   Range word combiners (between $200 and $300, from $200 to $300,        $200-$300);    -   Unit label and large-number-name abbreviation (e.g., 1-2        million, 1-2 inches, 1 million-2 million inches).

FIG. 5 depicts a view of a user interface for automatic type-basedgeneration of semantic variants of a natural language expression inaccordance with an illustrative embodiment. User interface 500illustrates the functionality in one application of generation of aquestion and answer pair lit. The user inputs a question, an answer, andselects an answer type from a list of previously created answer typesand a subtype, where applicable. The user may enter a question ID infield 501, or the question ID may be sequential numbers generated as thequestion and answer pair list is completed. The user may enter aquestion text in field 502 and answer text in field 503, or the questiontext and answer text may be entered from an existing question and answerpair list. The answer type and answer subtype may be entered using adrop-down list that is pre-populated based on a set of predeterminedtypes and subtypes.

The user may then select the “CREATE” button 504 to automaticallygenerate a list of answer variants that appear in field 505. The usermay add answer variants by entering text into field 506 and selectingthe “ADD ENTRY” button 508. The user may also clear the list of answervariants by selecting the “CLEAR” button 507, may delete an answervariant from the list of answer variants in field 505 by selecting the“DELETE ENTRY” button 510, and may save the list of answer variants byselecting the “SAVE” button 509. The list of answer variants may then beused to form an answer specification corresponding to the question textfor a question and answer pair.

FIGS. 6A-6C illustrate answer alternatives based on a plurality ofpolicies in accordance with an illustrative embodiment. In oneembodiment, the semantic variant generation component may specify aplurality of policies for providing answer alternatives. For example,for Date type answers, three policies may be provided as follows: apolicy to gradually back-off to a less specific granularity (e.g., the“General” policy), a policy of exact day match (the “Day” policy), and apolicy of exact year match (the “Year” policy). Each of the policies isappropriate to providing alternative answers to different kinds ofquestions. The first set of alternatives may be appropriate to aquestion such as “When did Germany invade Poland?” (where “Sep. 1,1939,” “9/1939,” and “1939” would all be appropriate answers). FIG. 6Aillustrates answer variants for the answer accordingly to the firstpolicy. The second policy might be appropriate for the question “On whatdate did Germany invade Poland?” (where only “Sep. 1, 1939” and variantsthereof would be appropriate). FIG. 6B illustrates answer variants forthe answer according to the second policy. The third policy would beappropriate to the question “In what year did Germany invade Poland?”FIG. 6C illustrates answer variants for the answer according to thethird policy.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

FIG. 7 is a flowchart illustrating operation of a type-based semanticvariant generation component in accordance with an illustrativeembodiment. Operation begins (block 700), and the semantic variantgeneration component receives a set of question and answer pairs (block701). The semantic variant generation component receives selection of aquestion and an answer to the selected question (block 702). Thesemantic variant generation component determines the type(s) andsubtypes(s) in the answer expression (block 703). The semantic variantgeneration component then performs user verification or correction ofthe type and subtype (block 704).

The semantic variant generation component performs rule-based expansionbased on the type and subtype (block 705). The type-specific rule-basedexpansions populate type-specific expansion templates at a level ofspecificity based on the input. The semantic variant generationcomponent then normalizes the regular expressions to account for specialcharacters (block 706).

The semantic variant generation component receives user modifications tothe list of answer variants (block 707). The user modifications mayinclude clearing the list of answer variants, adding an answer variantto the list, or deleting an answer variant from the list. The semanticvariant generation component determines whether the user selects a saveaction (block 708). If the user does not select a save action, thenoperation returns to block 707 to receive user modifications to the listof answer variants.

If the semantic variant generation component determines that a userselects a save action in block 708, then the semantic variant generationcomponent saves the list of answer variants to be used in a question andanswer pair for training a question answering machine learning model fora question answering system (block 709). The semantic variant generationcomponent determines whether an exit condition exists (block 710). Anexit condition may exist if the user closes the semantic variantgeneration component, for example. If an exit condition does not exist,then operation returns to block 703 to receive selection of anotherquestion and an answer to the selected question. If an exit conditionexists in block 710, then operation ends (block 711).

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Thus, the illustrative embodiments provide a mechanism for automatictype-based generation of semantic variants of natural languageexpressions for string or regular expression matching. The mechanismallows the user to use answer variant templates in an attempt to captureas many acceptable answers as possible while reducing the amount of usereffort, the possibility of leaving variants out, and the risk of errorsin the regular expression.

The illustrative embodiments make it much faster and more accurate todevelop answer variants, which is integral to the intended applicationof creating question and answer pair sets required to train answerranking models in an open domain question answering cognitive system.The illustrative embodiments also make it possible to flexibly produceanswer variants of various types. It is to be expected that training asystem with a wider range of automatically generated answers in thequestion and answer pair set should improve post-training accuracyquestions by several points.

As noted above, it should be appreciated that the illustrativeembodiments may take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In one example embodiment, the mechanisms of theillustrative embodiments are implemented in software or program code,which includes but is not limited to firmware, resident software,microcode, etc.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers. Network adapters mayalso be coupled to the system to enable the data processing system tobecome coupled to other data processing systems or remote printers orstorage devices through intervening private or public networks. Modems,cable modems and Ethernet cards are just a few of the currentlyavailable types of network adapters.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the describedembodiments. The embodiment was chosen and described in order to bestexplain the principles of the invention, the practical application, andto enable others of ordinary skill in the art to understand theinvention for various embodiments with various modifications as aresuited to the particular use contemplated. The terminology used hereinwas chosen to best explain the principles of the embodiments, thepractical application or technical improvement over technologies foundin the marketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

1. A method, in a data processing system having a processor and a memorystoring a store of semantic types and instructions for implementing anatural language processing engine for generating semanticallyequivalent variants of a natural language term, wherein the dataprocessing system configured with the natural language processing enginecomprises a question answering system, the method comprising: receiving,by the natural language processing engine, an input term for variantanalysis, wherein the input term is an answer to a question forming aquestion and answer pair for an answer key of the question answeringsystem; identifying, by the natural language processing engine executingon the data processing system, a semantic type of the input term basedon a store of semantic types; performing, by the natural languageprocessing engine, a type-specific series of rule-based expansions ofthe input term based on the identified semantic type of the input termto form a set of semantically equivalent variants of the input term;generating, by the natural language processing engine, a question andanswer pair using the question and each semantically equivalent variantwithin the set of semantically equivalent variants of the input term toform a supplemented answer key; training, by the natural languageprocessing engine, a question answering machine learning model for thequestion answering system using the supplemented answer key; andconfiguring the question answering system to operate in accordance withthe question answering machine learning model.
 2. (canceled)
 3. Themethod of claim 1, wherein stores of semantic types comprises a store ofa group consisting of a currency type, a person type, a percent type, adate type, a number type, a measurement type, or a range type.
 4. Themethod of claim 1, wherein the memory stores a store of units,conversions among units, and variants of unit names, the method furthercomprising extracting, by the natural language processing engine, aquantity and a unit from the input term based on the store of units,conversions among units, and variants of unit names.
 5. The method ofclaim 4, wherein performing a type-specific series of rule-basedexpansions comprises generating number variants based on the extractedquantity.
 6. The method of claim 5, wherein generating number variantscomprises performing at least one of rounding, varying degree ofprecision, varying formatting of numbers, or identifying word numbervariants.
 7. The method of claim 4, wherein performing a type-specificseries of rule-based expansions comprises performing unit conversionbased on the extracted unit.
 8. The method of claim 4, whereinperforming a type-specific series of rule-based expansions comprisesperforming unit label conversion based on the extracted unit.
 9. Themethod of claim 1, wherein performing a type-specific series ofrule-based expansions comprises performing word variants or datevariants.
 10. The method of claim 1, further comprising performingnormalization of regular expressions to account for special characters.11. A computer program product comprising: a computer readable storagemedium having a computer readable program stored therein, wherein thecomputer readable program, when executed on a data processing system,causes the data processing system to implement a natural languageprocessing engine, wherein the data processing system configured withthe natural language processing engine comprises a question answeringsystem, wherein the computer readable program causes the data processingsystem to: receive an input term for variant analysis, wherein the inputterm is an answer to a question forming a question and answer pair foran answer key of the question answering system; identify, by the naturallanguage processing engine executing on the data processing system, asemantic type of the input term based on a store of semantic types;perform, by the natural language processing engine, a type-specificseries of rule-based expansions of the input term based on theidentified semantic type of the input term to form a set of semanticallyequivalent variants of the input term; generate, by the natural languageprocessing engine, a question and answer pair using the question andeach semantically equivalent variant within the set of semanticallyequivalent variants of the input term to form a supplemented answer key;training, by the natural language processing engine, a questionanswering machine learning model for the question answering system usingthe supplemented answer key; and configuring the question answeringsystem to operate in accordance with the question answering machinelearning model.
 12. (canceled)
 13. The computer program product of claim11, wherein the computer readable storage medium has stored therein astore of units, conversions among units, and variants of unit names,wherein the natural language processing engine further causes the dataprocessing system to extract a quantity and a unit from the input termbased on the store of units, conversions among units, and variants ofunit names.
 14. The computer program product of claim 13, whereinperforming a type-specific series of rule-based expansions comprisesgenerating number variants based on the extracted quantity.
 15. Thecomputer program product of claim 14, wherein generating number variantscomprises performing at least one of rounding, varying degree ofprecision, varying formatting of numbers, or identifying word numbervariants.
 16. The computer program product of claim 13, whereinperforming a type-specific series of rule-based expansions comprisesperforming unit conversion based on the extracted unit.
 17. The computerprogram product of claim 13, wherein performing a type-specific seriesof rule-based expansions comprises performing unit label conversionbased on the extracted unit.
 18. The computer program product of claim11, wherein performing a type-specific series of rule-based expansionscomprises performing word variants or date variants.
 19. The computerprogram product of claim 11, wherein the natural language processingengine further causes the data processing system to performnormalization of regular expressions to account for special characters.20. An apparatus comprising: a processor; and a memory coupled to theprocessor, wherein the memory stores a computer readable program,wherein the computer readable program, when executed on the processor,causes the processor to implement a natural language processing engine,wherein the data processing system configured with the natural languageprocessing engine comprises a question answering system, wherein thecomputer readable program causes the processor to: receive, by thenatural language processing engine, an input term for variant analysis,wherein the input term is an answer to a question forming a question andanswer pair for an answer key of the question answering system;identify, by the natural language processing engine executing on thedata processing system, a semantic type of the input term based on astore of semantic types; perform, by the natural language processingengine, a type-specific series of rule-based expansions of the inputterm based on the identified semantic type of the input term to form aset of semantically equivalent variants of the input term; generate, bythe natural language processing engine, a question and answer pair usingthe question and each semantically equivalent variant within the set ofsemantically equivalent variants of the input term to form asupplemented answer key; train, by the natural language processingengine, a question answering machine learning model for the questionanswering system using the supplemented answer key; and configure thequestion answering system to operate in accordance with the questionanswering machine learning model.
 21. The method of claim 1, furthercomprising: generating, by the natural language processing engine, auser interface presenting the set of semantically equivalent variants ofthe input term; and receiving, by the natural language processingengine, user input modifying the set of semantically equivalent variantsof the input term via the user interface.
 22. The computer programproduct of claim 11, wherein the computer readable program furthercauses the data processing system to: generate, by the natural languageprocessing engine, a user interface presenting the set of semanticallyequivalent variants of the input term; and receive, by the naturallanguage processing engine, user input modifying the set of semanticallyequivalent variants of the input term via the user interface.